Proteomics - Analysis and integration of large-scale data sets
1. Proteomics Analysis and integration of large-scale data sets Lars Juhl Jensen EMBL Heidelberg
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3. Part 1 Methods for predicting protein-protein interactions Lars Juhl Jensen EMBL Heidelberg
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6. What is STRING? Genomic neighborhood Species co-occurrence Gene fusions Database imports Exp. interaction data Microarray expression data Literature co-mentioning
12. Formalizing the phylogenetic profile method Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps
13. Inferring functional associations from evolutionarily conserved operons Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species
14. Predicting functional and physical interactions from gene fusion/fission events Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments
15. Integrating physical interaction screens Make binary representation of complexes Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments
16. Mining microarray expression databases Re-normalize arrays by modern method to remove biases Build expression matrix Combine similar arrays by PCA Construct predictor by Gaussian kernel density estimation Calibrate against KEGG maps Infer associations in other species
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18. Non-linear normalization of intensities and correction for spatial effects Downloaded SMD data After intensity normalization Spatial bias estimate After spatial normalization
19. Co-mentioning in the scientific literature Associate abstracts with species Identify gene names in title/abstract Count (co-)occurrences of genes Test significance of associations Calibrate against KEGG maps Infer associations in other species
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21. The power of cross-species transfer and evidence integration
22. The power of cross-species transfer and evidence integration
23. The power of cross-species transfer and evidence integration
24. The power of cross-species transfer and evidence integration
25. The power of cross-species transfer and evidence integration
26. The power of cross-species transfer and evidence integration
60. Part 4 Qualitative modeling of the of the yeast cell cycle Lars Juhl Jensen EMBL Heidelberg
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64. Getting the parts list yeast culture Microarrays Gene expression Expression profile Ulrik de Lichtenberg, CBS, DTU Lyngby Cho et al. & Spellman et al. 600 periodically expressed genes (with associated peak times) that encode “dynamic proteins” The Parts list New Analysis